{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "80c079a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\owner\\anaconda3\\lib\\site-packages\\scipy\\__init__.py:138: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.3)\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion} is required for this version of \"\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import datetime as timedelta\n",
    "import matplotlib.mlab as ml\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e5f06a56",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Figure 7.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ed6a5bfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,1))\n",
    "plt.rc('font', size = 15)\n",
    "plt.rc('axes', labelsize = 15)\n",
    "plt.rc('xtick', labelsize = 15)\n",
    "plt.rc('ytick', labelsize = 15)\n",
    "plt.rc('legend', fontsize = 15)\n",
    "plt.rc('figure', titlesize = 15)\n",
    "\n",
    "\n",
    "plt.axvline(1, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(32, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(63, color='gray',  linewidth=1, alpha=0.5)\n",
    "plt.axvline(93, color='gray',  linewidth=1, alpha=0.5)\n",
    "plt.axvline(124, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(154, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(185, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(216, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(244, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(275, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(305, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(336, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(366, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(397, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(428, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(458, color='gray', linewidth=1, alpha=0.5)\n",
    "\n",
    "\n",
    "\n",
    "plt.bar(df[\"Number\"],df[\"Precipitation\"], color = 'blue', alpha=1,width = 1,zorder=2)\n",
    "\n",
    "plt.xticks([1,32,63,93,124,154,185,216,244,275,305,336,366,397,428,458], \n",
    "           labels = ['','','','','','','','','','','','','','','',''])\n",
    "\n",
    "plt.yticks([0, 40, 80, 120,160], labels = ['','','','',''])\n",
    "plt.xlim([1, 428])\n",
    "plt.ylim([0, 160])\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(axis = 'y', alpha=0.5, color='gray')\n",
    "\n",
    "\n",
    "plt.gca().invert_yaxis()\n",
    "\n",
    "plt.title(\"\", loc = 'center')\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"\")\n",
    "#plt.colorbar()\n",
    "#plt.legend(loc = 2, bbox_to_anchor = (1,1))\n",
    "plt.savefig('Precipitation.png',bbox_inches = \"tight\", dpi = 600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fde678a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,1))\n",
    "plt.rc('font', size = 15)\n",
    "plt.rc('axes', labelsize = 15)\n",
    "plt.rc('xtick', labelsize = 15)\n",
    "plt.rc('ytick', labelsize = 15)\n",
    "plt.rc('legend', fontsize = 15)\n",
    "plt.rc('figure', titlesize = 15)\n",
    "\n",
    "\n",
    "plt.axvline(1, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(32, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(63, color='gray',  linewidth=1, alpha=0.5)\n",
    "plt.axvline(93, color='gray',  linewidth=1, alpha=0.5)\n",
    "plt.axvline(124, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(154, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(185, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(216, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(244, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(275, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(305, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(336, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(366, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(397, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(428, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(458, color='gray', linewidth=1, alpha=0.5)\n",
    "\n",
    "plt.plot(df[\"Number\"],df[\"Mean T\"], color = 'black', alpha=0.5,linewidth = 1)\n",
    "plt.plot(df[\"Number\"],df[\"Min T\"], color = 'blue', alpha=0.5,linewidth = 1)\n",
    "plt.plot(df[\"Number\"],df[\"Max T\"], color = 'red', alpha=0.5,linewidth = 1)\n",
    "\n",
    "plt.scatter(df[\"N\"],df[\"WT\"], color = 'white', s = 15, alpha=1,edgecolor='black',zorder=10)\n",
    "\n",
    "\n",
    "plt.xticks([1,32,63,93,124,154,185,216,244,275,305,336,366,397,428,458], \n",
    "           labels = ['','','','','','','','','','','','','','','',''])\n",
    "\n",
    "plt.yticks([-10, 0, 10, 20,30,40], labels = ['','','','','',''])\n",
    "plt.xlim([1, 428])\n",
    "plt.ylim([-10, 40])\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(axis = 'y', alpha=0.5, color='gray')\n",
    "\n",
    "plt.title(\"\", loc = 'center')\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"\")\n",
    "#plt.colorbar()\n",
    "#plt.legend(loc = 2, bbox_to_anchor = (1,1))\n",
    "plt.savefig('Temp.png',bbox_inches = \"tight\", dpi = 600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "df8d805b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,1))\n",
    "plt.rc('font', size = 15)\n",
    "plt.rc('axes', labelsize = 15)\n",
    "plt.rc('xtick', labelsize = 15)\n",
    "plt.rc('ytick', labelsize = 15)\n",
    "plt.rc('legend', fontsize = 15)\n",
    "plt.rc('figure', titlesize = 15)\n",
    "\n",
    "\n",
    "plt.vlines(16, 3.0648, 7.4672, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(23, 4.5646, 8.2232, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(29, 4.0574, 8.1, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(42, 0.8986, 5.334, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(58, 6.1377, 9.4085, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(65, 4.2397, 9.0455, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(72, 3.1092, 7.1882, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(93, 5.1233, 8.1677, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(100, 4.1367, 8.3375, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(107, 4.8118, 9.213, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(114, 3.0066, 7.8924, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(121, 3.4491, 7.9341, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(128, 4.0661, 7.6931, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(142, 3.809, 8.1014, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(149, 4.7044, 8.0958, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(163, 3.7474, 7.6314, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(177, 4.4709, 8.2203, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(201, 6.9131, 7.4235, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(226, 4.3882, 7.984, color='black', linestyle='solid', linewidth=1)\n",
    "\n",
    "plt.vlines(254, 3.4117, 7.672934, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(268, 5.008997, 8.778448, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(289, 4.729308, 8.781517, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(296, 5.083597, 8.778897, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(310, 4.777716, 8.583114, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(324, 4.622034, 7.305163, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(338, 4.0265, 6.314342, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(352, 3.089806, 6.006749, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(366, 3.259879, 5.9236, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(384, 3.352425, 5.73483, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(408, 2.548026, 4.557, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(421, 2.569429, 4.314517, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(422, 2.886085, 4.031748, color='black', linestyle='solid', linewidth=1)\n",
    "\n",
    "\n",
    "\n",
    "plt.axvline(1, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(32, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(63, color='gray',  linewidth=1, alpha=0.5)\n",
    "plt.axvline(93, color='gray',  linewidth=1, alpha=0.5)\n",
    "plt.axvline(124, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(154, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(185, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(216, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(244, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(275, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(305, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(336, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(366, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(397, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(428, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(458, color='gray', linewidth=1, alpha=0.5)\n",
    "\n",
    "\n",
    "plt.scatter(df[\"N\"],df[\"Depth\"], color = 'white', s = 15, alpha=1,edgecolor='black',zorder=2)\n",
    "plt.plot(df[\"Number\"],df[\"Public depth\"], color = 'red', alpha=0.5,linewidth = 1)\n",
    "\n",
    "\n",
    "plt.xticks([1,32,63,93,124,154,185,216,244,275,305,336,366,397,428,458], \n",
    "           labels = ['','','','','','','','','','','','','','','',''])\n",
    "plt.yticks([ 0, 2, 6, 4,8,10,12], labels = ['','','','','','',''])\n",
    "plt.xlim([1, 428])\n",
    "plt.ylim([0, 12])\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(axis = 'y', alpha=0.5, color='gray')\n",
    "\n",
    "plt.title(\"\", loc = 'center')\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"\")\n",
    "#plt.colorbar()\n",
    "#plt.legend(loc = 2, bbox_to_anchor = (1,1))\n",
    "plt.savefig('Depth.png',bbox_inches = \"tight\", dpi = 600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "64f8513b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,1))\n",
    "plt.rc('font', size = 15)\n",
    "plt.rc('axes', labelsize = 15)\n",
    "plt.rc('xtick', labelsize = 15)\n",
    "plt.rc('ytick', labelsize = 15)\n",
    "plt.rc('legend', fontsize = 15)\n",
    "plt.rc('figure', titlesize = 15)\n",
    "\n",
    "\n",
    "plt.vlines(16, 0.667503, 1.033332, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(23, 0.667503, 1.221389, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(29, 0.618653, 1.226039, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(42, 0.706656, 1.033332, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(58, 2.661764, 4.027674, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(65, 2.893853, 4.18367, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(72, 2.312638, 3.407534, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(93, 2.095074, 3.280477, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(100, 2.74392, 3.48612, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(107, 2.19282, 3.06359, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(114, 2.34806, 2.97186, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(121, 1.76577, 3.00594, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(128, 1.84816, 4.07385, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(142, 1.89078, 4.24775, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(149, 1.9052, 4.07385, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(163, 1.79964, 3.62114, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(177, 1.89078, 3.89226, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(201, 2.10305, 3.19437, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(226, 3.20653, 4.58316, color='black', linestyle='solid', linewidth=1)\n",
    "\n",
    "plt.vlines(254, 3.182251, 3.580094, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(268, 3.676602, 4.963886, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(289, 3.634927, 4.3623254, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(296, 3.407534, 4.445929, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(310, 3.318089, 4.199598, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(324, 3.243292, 4.104931, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(338, 3.292967, 3.848136, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(352, 2.796557, 3.593724, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(366, 1.897978, 2.50475, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(384, 1.636548, 2.485786, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(408, 1.386447, 1.704283, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(421, 1.162351, 1.305446, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(422, 1.25589, 1.333817, color='black', linestyle='solid', linewidth=1)\n",
    "\n",
    "plt.vlines(435, 0.909686, 1.127885, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(443, 1.930626, 2.746821, color='black', linestyle='solid', linewidth=1)\n",
    "plt.vlines(450, 2.488162, 2.586342, color='black', linestyle='solid', linewidth=1)\n",
    "\n",
    "\n",
    "plt.fill([1, 32, 32, 1], [0.3, 0.3, 3.2, 3.2], color='red', alpha=0.3)\n",
    "plt.fill([32, 63, 63, 32], [0.346, 0.346, 2.2, 2.2], color='red', alpha=0.3)\n",
    "plt.fill([63, 93, 93, 63], [0.250, 0.250, 3.2, 3.2], color='red', alpha=0.3)\n",
    "plt.fill([93, 124, 124, 93], [0.15, 0.15, 3.2, 3.2], color='red', alpha=0.3)\n",
    "plt.fill([124, 154, 154, 124], [0.424, 0.424, 3.3, 3.3], color='red', alpha=0.3)\n",
    "plt.fill([154, 185, 185, 154], [0.374, 0.374, 3.4, 3.4], color='red', alpha=0.3)\n",
    "plt.fill([185, 216, 216, 185], [0.376, 0.376, 3.8, 3.8], color='red', alpha=0.3)\n",
    "plt.fill([216, 244, 244, 216], [0.35, 0.35, 3.7, 3.7], color='red', alpha=0.3)\n",
    "plt.fill([244, 275, 275, 244], [0.3, 0.3, 3.5, 3.5], color='red', alpha=0.3)\n",
    "plt.fill([275, 305, 305, 275], [0.203, 0.203, 3.82, 3.82], color='red', alpha=0.3)\n",
    "plt.fill([305, 336, 336, 305], [0.453, 0.453, 2.1, 2.1], color='red', alpha=0.3)\n",
    "plt.fill([336, 366, 366, 336], [0.246, 0.246, 1.731, 1.731], color='red', alpha=0.3)\n",
    "plt.fill([366, 397, 397, 366], [0.27, 0.27, 2, 2], color='red', alpha=0.3)\n",
    "plt.fill([397, 428, 428, 397], [0.4, 0.4, 1.8, 1.8], color='red', alpha=0.3)\n",
    "\n",
    "\n",
    "\n",
    "plt.axvline(1, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(32, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(63, color='gray',  linewidth=1, alpha=0.5)\n",
    "plt.axvline(93, color='gray',  linewidth=1, alpha=0.5)\n",
    "plt.axvline(124, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(154, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(185, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(216, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(244, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(275, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(305, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(336, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(366, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(397, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(428, color='gray', linewidth=1, alpha=0.5)\n",
    "plt.axvline(458, color='gray', linewidth=1, alpha=0.5)\n",
    "\n",
    "\n",
    "plt.scatter(df[\"N\"],df[\"NO3\"], color = 'white', s = 15, alpha=1,edgecolor='black',zorder=2)\n",
    "\n",
    "\n",
    "plt.xticks([1,32,63,93,124,154,185,216,244,275,305,336,366,397,428,458], \n",
    "           labels = ['','','','','','','','','','','','','','','',''])\n",
    "\n",
    "plt.yticks([ 0, 1, 2, 3,4,5], labels = ['','','','','',''])\n",
    "plt.xlim([1, 428])\n",
    "plt.ylim([0, 5])\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(axis = 'y', alpha=0.5, color='gray')\n",
    "\n",
    "plt.title(\"\", loc = 'center')\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"\")\n",
    "#plt.colorbar()\n",
    "#plt.legend(loc = 2, bbox_to_anchor = (1,1))\n",
    "plt.savefig('Nitrate.png',bbox_inches = \"tight\", dpi = 600)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
